@inproceedings{1c9e1e9ea0454ea69db0908c21490e36,
title = "Toward Ontology Representation and Reasoning for News",
abstract = "Most research work on news mining nowadays covers phrase and topic level. A few works conducted on logical level mainly focus on personalized news service and no special efforts are put on the applications of ontology techniques on deep news mining. In this paper, we demonstrate a whole strategy for deeply understanding event-focused news taking the advantage of ontology representation and ontology reasoning. We propose an ontology-enriched news deep understanding framework ONDU which addresses the following problems: (1) how to transfer parsed news content into logical triples by using domain ontology. (2) The application of ONDU based on the reasoning results from the ontology reasoner TrOWL over the RDF data expressing the news. Through this whole strategy we can detect the inconsistence among multiple news articles and compare the different information implied in different news. We can even integrate a set of news content through merging the RDF data. The empirical experiment conducted on news from several portals shows the effectiveness and usefulness of our method.",
keywords = "News mining, Ontology, Reasoning, Text understanding, TrOWL",
author = "Xubo Wen and Xiaoli Ma and Juanzi Li and Pan, {Jeff Z.} and Jiayu Xie",
note = "The work is supported by the Natural Science Foundation of China (No. 61035004, No. 60973102), 863 High Technology Program (2011AA01A207), European Union 7th framework project FP7-288342, and THUNUS NExT Co-Lab. ; 7th Chinese Semantic Web Symposium and the 2nd Chinese Web ScienceConference, CSWS 2013 ; Conference date: 12-08-2013 Through 16-08-2013",
year = "2013",
month = nov,
doi = "10.1007/978-3-642-54025-7_16",
language = "English",
isbn = "9783642540240",
series = "Communications in Computer and Information Science",
publisher = "Springer-Verlag",
pages = "186--198",
editor = "Guilin Qi and Jie Tang and Jianfeng Du and Pan, {Jeff Z.} and Yong Yu",
booktitle = "Linked Data and Knowledge Graph",
}